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<div class=Section1>

<h1><a name="_Toc111992856"></a><a name="_Toc112582956"></a><a
name="_Princeton_multi-voxel_pattern"></a><a name="__Data_structures"></a><a
name="__Statmaps"></a><a name="__4._Masks"></a><a name="__Peeking"></a><span
style='mso-bookmark:_Toc111992856'><span style='mso-bookmark:_Toc112582956'>Princeton
multi-voxel pattern analysis</span></span> &#8211; glossary</h1>

<p class=MsoNormal align=right style='margin-top:2.0pt;mso-margin-bottom-alt:
auto;text-align:right'><a href="http://www.csbmb.princeton.edu/mvpa">http://www.csbmb.princeton.edu/mvpa</a></p>

<p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><![if !supportEmptyParas]>&nbsp;<![endif]><o:p></o:p></p>

<p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span
lang=EN-US style='mso-ansi-language:EN-US'>See <a
href="manual.htm#__Data_structures">Data structures</a> for more information on
terms relating to the way the data is stored by the toolbox.<u1:p></u1:p><span
style='color:red'><o:p></o:p></span></span></p>

<p class=MsoNormal><![if !supportEmptyParas]>&nbsp;<![endif]><o:p></o:p></p>

<h2><a name="_block"></a><span lang=EN-US style='mso-ansi-language:EN-US'>block<u1:p></u1:p><o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>A group of
contiguous <a href="#_TR">TR</a>s from the same <a href="#_condition">condition</a>
in a particular <a href="#_run">run</a>. Usually comprises multiple behavioural
<a href="#_trial">trials</a>.</span></p>

<h2><a name="_condition"></a><span lang=EN-US style='mso-ansi-language:EN-US'>classification<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>In the
machine learning sense, classification means taking a labelled training data
set and showing the classifier algorithm examples of each condition over and
over until it can successfully identify the training data. Then, the
classifier&#8217;s generalization performance is tested by asking it to guess the
conditions of new, unseen data points.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: <a
href="manual.htm#_Classification">Classification section</a> in the manual<o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>condition<u1:p></u1:p><o:p></o:p></span></h2>

<p class=MsoNormal>The groups that you&#8217;re trying to teach your classifier to
distinguish, e.g. different tasks being performed by the subject in the
experiment, or different stimuli being viewed.</p>

<h2><a name="_header"></a><a name="_cross-validation"></a><span lang=EN-US
style='mso-ansi-language:EN-US'>cross-validation<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>When you
use n-minus-one/leave-one-out cross-validation classification, you iterate over
your data multiple times. Each <a href="#_iteration">iteration</a> involves a
fresh classifier <a href="#_condition">trained</a> on a subset of the data, and
tested on the withheld data.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: <a
href="#_N-minus-one_(leave-one-out)_cross-valid">N-minus-one (leave-one-out)
cross-validation</a><o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>feature selection<o:p></o:p></span></h2>

<p><span lang=EN-US style='mso-ansi-language:EN-US'>Deciding which of your
features (e.g. voxels) you want to include in your analysis.<o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>generalization<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>Testing the
performance of a trained classifier on previously-unseen (test) data<o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>header<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: <a
href="#_Book-keeping_and_the">Data structure &#8211; Book-keeping and the headers</a><o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>history<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>A free-text
field in the <a href="#_header">header</a> that gets automatically appended to,
creating a sort of narrative of that object&#8217;s role in the analysis.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See <a
href="#_Book-keeping_and_the">Data structure &#8211; Book-keeping and the headers</a><o:p></o:p></span></p>

<h2><a name="_iteration"></a><span lang=EN-US style='mso-ansi-language:EN-US'>iteration<u1:p></u1:p><o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>Running the
classifier once, using a particular subset of the data for testing, and the
remainder for training. For example, you have 10 runs, you'll have 10
iterations, each time withholding a different run as the testing data.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: <a
href="#_n-minus-one_cross-validation_1">Glossary &#8211; n-minus-one</a> and <a
href="#_N-minus-one_(leave-one-out)_cross-valid">Classification &#8211; N-minus-one</a>.<o:p></o:p></span></p>

<h2><a name="_mask"></a><span lang=EN-US style='mso-ansi-language:EN-US'>leave-one-out<o:p></o:p></span></h2>

<p><span lang=EN-US style='mso-ansi-language:EN-US'>We use 'leave-one-out' and
'n-minus-one' interchangeably to refer to the <a href="#_cross-validation">cross-validation</a>
procedure that leaves out a different subsection (e.g. <a href="#_run">run</a>)
of the data each <a href="#_iteration">iteration</a>.<o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>mask<u1:p></u1:p><o:p></o:p></span></h2>

<p><span lang=EN-US style='mso-ansi-language:EN-US'>A boolean 3D (or maybe 2D)
single-TR volume indicating which voxels are to be included.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See </span><a
href="#_mask">Data structure &#8211; masks</a><span lang=EN-US style='mso-ansi-language:
EN-US'>.</span></p>

<h2><a name="_n-minus-one_cross-validation"></a><a name="_name"></a><span
lang=EN-US style='mso-ansi-language:EN-US'>name<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>Every <a
href="#_object">object</a> in the <span style='font-variant:small-caps'><a
href="#_subj"><i style='mso-bidi-font-style:normal'><span style='font-variant:
normal'>subj</span></i></a></span> structure has a name. This is a very
important field, since it is used whenever accessing that object. The user is
advised to refrain from accessing objects directly (e.g. subj.patterns{1}).<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: <a
href="#_The_innards_of">Data structure &#8211; innards of the <i style='mso-bidi-font-style:
normal'>subj</i> structure</a> and <a href="#_Accessing_the_subj">Advanced &#8211;
accessing <i style='mso-bidi-font-style:normal'>subj</i> directly</a><o:p></o:p></span></p>

<h2><a name="_n-minus-one_cross-validation_1"></a><span lang=EN-US
style='mso-ansi-language:EN-US'>n-minus-one cross-validation<u1:p></u1:p><o:p></o:p></span></h2>

<p><span lang=EN-US style='mso-ansi-language:EN-US'>We use 'leave-one-out' and
'n-minus-one' interchangeably to refer to the <a href="#_cross-validation">cross-validation</a>
procedure that leaves out a different subsection (e.g. <a href="#_run">run</a>)
of the data each <a href="#_iteration">iteration</a>.<o:p></o:p></span></p>

<h2><a name="_object"></a><span lang=EN-US style='mso-ansi-language:EN-US'>object<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>An example
of one of the <a href="#_Data_structure">4 main data types</a>, e.g. a single
cell in <i style='mso-bidi-font-style:normal'>subj</i>.<i style='mso-bidi-font-style:
normal'>patterns</i> or <i style='mso-bidi-font-style:normal'>subj.masks</i>.
Contains a <i style='mso-bidi-font-style:normal'>mat</i> field with all the
data, as well other required fields such as <a href="#_name">name</a>,
group_name, derived_from, <a href="#_header">header</a> etc.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: <a
href="#_The_innards_of">The innards of the subj structure</a><o:p></o:p></span></p>

<h2><a name="_pattern"></a><span lang=EN-US style='mso-ansi-language:EN-US'>one-of-n<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>In this
toolbox, this tends to refer a regressors matrix, to the idea that only a single
condition can be active at any timepoint. This makes sense for basic/standard
classification &#8211; each timepoint belongs to one or other of the conditions, but
not more than one at once. <o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>Convolving
regressors with a hemodynamic response function will lead to continuous-valued
regressors, which may overlap (i.e. more than one condition may be non-zero at
a given timepoint), which may violate some functions' one-of-n requirements.<o:p></o:p></span></p>

<p class=MsoNormal><i><span lang=EN-US style='mso-ansi-language:EN-US'><a
href="m2html\check_1ofn_regressors.html">Check_1ofn_regressors.m</a></span></i><span
lang=EN-US style='mso-ansi-language:EN-US'> allows you to test whether a matrix
is one-of-n.<o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>pattern<u1:p></u1:p><o:p></o:p></span></h2>

<p class=MsoNormal>A (features x timepoints) matrix, usually of voxel
activities, but could also be PCA components, wavelet coefficients, GLM beta
weights or a statmap.</p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: </span><a
href="#_pattern">Data structure &#8211; patterns</a><span lang=EN-US
style='mso-ansi-language:EN-US'>.</span></p>

<h2><a name="_regressors"></a><span lang=EN-US style='mso-ansi-language:EN-US'>peeking<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>When you
use your testing data set to help with voxel selection. Basically, this is a
kind of cheating, and spuriously/illegitimately improves your classification by
some margin.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: <a
href="manual.htm\#__Peeking">Pre-classification &#8211; peeking</a>.<o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>performance<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>The
performance metric measures the similarity between the output produced by a
classifier to the output it's supposed to produce.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See <a
href="manual.htm#_Performance">Performance</a> in the Classification section of
the manual.<o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>pre-classification<o:p></o:p></span></h2>

<p><span lang=EN-US style='mso-ansi-language:EN-US'>By this, we mean the
normalization and feature selection steps that go on before after the data
structure has been created but before beginning classification, e.g. <i><a
href="m2html\zscore_runs.html">zscore_runs.m</a></i> and <i><a
href="m2html\feature_select.html">feature_select.m</a></i>.<o:p></o:p></span></p>

<p><span lang=EN-US style='mso-ansi-language:EN-US'>See <a
href="manual.htm#_Pre-classification">pre-classification</a>.<o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>regressors<u1:p></u1:p><o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>For our
purposes, the term &#8216;regressors&#8217; refers to a set of values for each TR that denote
the extent to which each condition is active. Used by statistical tests, and
also as the teacher signal for the classifiers.</span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: </span><a
href="#_regressors">Data structure &#8211; regressors</a><span lang=EN-US
style='mso-ansi-language:EN-US'>.</span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>results<u1:p></u1:p><o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>This is
where all the information about <a href="#_condition">classification</a> is
stored.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: <a
href="#_The_results_structure">Classification &#8211; results structure</a>.</span></p>

<h2><a name="_run"></a><span lang=EN-US style='mso-ansi-language:EN-US'>run<u1:p></u1:p><o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>A single
scanning session. There are usually a handful of runs in a given hour-long
experiment.</span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>selector<u1:p></u1:p><o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>A set of labels
for each <a href="#_TR">TR</a>, e.g. where all the <a href="#_run">runs</a>
start and finish, or which TRs should be used for <a href="#_condition">training</a>
and which for testing on this <a href="#_iteration">iteration</a>.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: </span><a
href="#_3._Selectors">Data structure &#8211; selectors</a><span lang=EN-US
style='mso-ansi-language:EN-US'>.</span></p>

<h2><a name="_statmap"></a><span lang=EN-US style='mso-ansi-language:EN-US'>statmap<o:p></o:p></span></h2>

<p class=MsoNormal style='margin-top:4.0pt'><span lang=EN-US style='mso-ansi-language:
EN-US'>The result of some kind of statistical test, usually performed
separately for each voxel. For instance, the ANOVA yields a statmap of p values,
one for each voxel. Each p value denotes the probability that that voxel varies
significantly between conditions.<o:p></o:p></span></p>

<p class=MsoNormal style='margin-top:4.0pt'><span lang=EN-US style='mso-ansi-language:
EN-US'>Statmaps are stored as patterns, since the term &#8216;mask&#8217; is usually used
to refer to a boolean 3D volume.<o:p></o:p></span></p>

<p class=MsoNormal style='margin-top:4.0pt'><span lang=EN-US style='mso-ansi-language:
EN-US'>A mask can be created from a statmap by choosing all the voxels that are
above/below some threshold.<o:p></o:p></span></p>

<p class=MsoNormal style='margin-top:4.0pt'><span lang=EN-US style='mso-ansi-language:
EN-US'>See <a href="manual.htm#__4._Masks">Data structure &#8211; masks</a> and <a
href="manual.htm#__Statmaps">Pre-classification &#8211; Statmaps</a>.<o:p></o:p></span></p>

<h2><a name="_subj"></a><span lang=EN-US style='mso-ansi-language:EN-US'>subj<u1:p></u1:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See: </span><a
href="#_3._Selectors">Data structure &#8211; selectors</a><span lang=EN-US
style='mso-ansi-language:EN-US'>.</span></p>

<h2><a name="_testing"></a><a name="_TR"></a><span lang=EN-US style='mso-ansi-language:
EN-US'>testing<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>Presented a
<a href="#_training">trained</a> classifier with patterns that it has never
seen before, and testing its performance.<o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>TR<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>Stands for
&#8216;time to repetition&#8217;. Basically, the time taken for the scanner to acquire a
single 3D brain volume. Used in this manual to refer to a single timepoint.<o:p></o:p></span></p>

<h2><a name="_training"></a><a name="_trial"></a><span lang=EN-US
style='mso-ansi-language:EN-US'>training<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>Showing a
classifier lots of examples of a person's brain in condition A, and telling it
each time, 'This is an example of the brain in condition A'. We then show it
lots of examples of the same brain in condition B, also telling it which
condition these brain examples came from. This process repeats until the
classifier has learned which are which.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>In reality,
the examples tend to be interleaved with each other and presented in a
different order each time. Most classifier algorithms can also deal with more
than just two categories.<o:p></o:p></span></p>

<h2><span lang=EN-US style='mso-ansi-language:EN-US'>trial<o:p></o:p></span></h2>

<p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span
lang=EN-US style='mso-ansi-language:EN-US'>A behavioural trial in the
experiment, that probably spans multiple <a href="#_TR">TR</a>s. Multiple
trials make up a <a href="#_block">block</a>.</span></p>

<h2><a name="_voxel_selection"></a><span lang=EN-US style='mso-ansi-language:
EN-US'>voxel selection<o:p></o:p></span></h2>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>Whenever
you apply a <i style='mso-bidi-font-style:normal'>mask</i> to a <i
style='mso-bidi-font-style:normal'>pattern</i>, you are selecting voxels. This
term tends to be used more often in the machine learning context of &#8216;feature
selection&#8217; &#8211; choosing which of the features (voxels) contain signal for the
classification problem you are attempting.<o:p></o:p></span></p>

<p class=MsoNormal><span lang=EN-US style='mso-ansi-language:EN-US'>See:
'Pre-classification &#8211; Anova' in the <a href="manual.htm">manual</a>.<o:p></o:p></span></p>

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